Smooth and sparse hyperspectral unmixing using an l0 penalty

Hyperspectral unmixing is an important technique for analyzing hyperspectral remote sensing images. We propose an estimation algorithm that, simultaneously, encourages smoothness in the endmembers and sparseness in the abundances by using first order roughness and l0 penalties. The method is evaluated both on simulated data and a real hyperspectral image of an urban landscape.

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